[Notebook] Create notebook_en and modify notebook_ch

Translate all notebooks into English

notebook/notebook_ch/2.text_detection/文本检测FAQ.ipynb

@@ -81,7 +81,7 @@
     "\n",
     "如果对某些层使用更小的学习率学习，静态图里还不是很方便，一个方法是在参数初始化的时候，给权重的属性设置固定的学习率，参考：https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/fluid/param_attr/ParamAttr_cn.html#paramattr\n",
     "\n",
-    "实际上我们实验发现，直接加载模型去fine-tune，不设置某些层不同学习率，效果也都不错\n",
+    "实际上我们实验发现，直接加载模型去fine-tune，不设置某些层不同学习率，效果也都不错。\n",
     "\n",
     "**1.11 DB的预处理部分，图片的长和宽为什么要处理成32的倍数？**\n",
     "\n",
@@ -95,7 +95,7 @@
     "\n",
     "**1.13 PP-OCR检测效果不好，该如何优化？**\n",
     "\n",
-    "A： 具体问题具体分析:\n",
+    "**A**： 具体问题具体分析:\n",
     "- 如果在你的场景上检测效果不可用，首选是在你的数据上做finetune训练；\n",
     "- 如果图像过大，文字过于密集，建议不要过度压缩图像，可以尝试修改检测预处理的resize逻辑，防止图像被过度压缩；\n",
     "- 检测框大小过于紧贴文字或检测框过大，可以调整db_unclip_ratio这个参数，加大参数可以扩大检测框，减小参数可以减小检测框大小；\n",
@@ -123,8 +123,8 @@
     "\n",
     "**A**：GPU加速预测推荐使用TensorRT。\n",
     "- 1. 从[链接](https://paddleinference.paddlepaddle.org.cn/master/user_guides/download_lib.html)下载带TensorRT的Paddle安装包或者预测库。\n",
-    "- 2. 从Nvidia官网下载TensorRT版本，注意下载的TensorRT版本与paddle安装包中编译的TensorRT版本一致。\n",
-    "- 3. 设置环境变量LD_LIBRARY_PATH，指向TensorRT的lib文件夹\n",
+    "- 2. 从Nvidia官网下载[TensorRT](https://developer.nvidia.com/tensorrt)，注意下载的TensorRT版本与paddle安装包中编译的TensorRT版本一致。\n",
+    "- 3. 设置环境变量`LD_LIBRARY_PATH`，指向TensorRT的lib文件夹\n",
     "```\n",
     "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<TensorRT-${version}/lib>\n",
     "```\n",

notebook/notebook_ch/2.text_detection/文本检测理论篇.ipynb

@@ -6,7 +6,7 @@
     "collapsed": false
    },
    "source": [
-    "# OCR七日课之文本检测综述\n"
+    "# 文本检测算法理论\n"
    ]
   },
   {
@@ -15,11 +15,11 @@
     "collapsed": false
    },
    "source": [
-    "## 1. 文本检测\n",
+    "## 1 文本检测\n",
     "\n",
     "文本检测任务是找出图像或视频中的文字位置。不同于目标检测任务，目标检测不仅要解决定位问题，还要解决目标分类问题。\n",
     "\n",
-    "文本在图像中的表现形式可以视为一种‘目标‘，通用的目标检测的方法也适用于文本检测，从任务本身上来看：\n",
+    "文本在图像中的表现形式可以视为一种‘目标’，通用的目标检测的方法也适用于文本检测，从任务本身上来看：\n",
     "\n",
     "- 目标检测：给定图像或者视频，找出目标的位置（box），并给出目标的类别；\n",
     "- 文本检测：给定输入图像或者视频，找出文本的区域，可以是单字符位置或者整个文本行位置；\n",
@@ -41,14 +41,14 @@
     "1. 自然场景中文本具有多样性：文本检测受到文字颜色、大小、字体、形状、方向、语言、以及文本长度的影响；\n",
     "2. 复杂的背景和干扰；文本检测受到图像失真，模糊，低分辨率，阴影，亮度等因素的影响；\n",
     "3. 文本密集甚至重叠会影响文字的检测；\n",
-    "4. 文字存在局部一致性，文本行的一小部分，也可视为是独立的文本；\n",
+    "4. 文字存在局部一致性：文本行的一小部分，也可视为是独立的文本。\n",
     "\n",
     "<center><img src=\"https://ai-studio-static-online.cdn.bcebos.com/072f208f2aff47e886cf2cf1378e23c648356686cf1349c799b42f662d8ced00\"\n",
     "width=\"1000\" ></center>\n",
     "\n",
     "<br><center>图3 文本检测场景</center>\n",
     "\n",
-    "针对以上问题，衍生了很多基于深度学习的文本检测算法，解决自然场景文字检测问题，这些方法可以分为基于回归和基于分割的文本检测方法。\n",
+    "针对以上问题，衍生出了很多基于深度学习的文本检测算法，用于解决自然场景文字检测问题。这些方法可以分为基于回归和基于分割的文本检测方法。\n",
     "\n",
     "下一节将简要介绍基于深度学习技术的经典文字检测算法。"
    ]
@@ -59,7 +59,7 @@
     "collapsed": false
    },
    "source": [
-    "## 2. 文本检测方法介绍\n",
+    "## 2 文本检测方法介绍\n",
     "\n",
     "\n",
     "近些年来基于深度学习的文本检测算法层出不穷，这些方法大致可以分为两类：\n",
@@ -134,7 +134,7 @@
     "\n",
     "\n",
     "\n",
-    "LOMO[19]针对长文本和弯曲文本问题，提出迭代的优化文本定位特征获取更精细的文本定位，该方法包括三个部分，坐标回归模块DR，迭代优化模块IRM以及任意形状表达模块SEM。分别用于生成文本大致区域，迭代优化文本定位特征，预测文本区域、文本中心线以及文本边界。迭代的优化文本特征可以更好的解决长文本定位问题以及获得更精确的文本区域定位。\n",
+    "LOMO[19]针对长文本和弯曲文本问题，提出迭代的优化文本定位特征获取更精细的文本定位。该方法包括三个部分：坐标回归模块DR，迭代优化模块IRM以及任意形状表达模块SEM。它们分别用于生成文本大致区域，迭代优化文本定位特征，预测文本区域、文本中心线以及文本边界。迭代的优化文本特征可以更好的解决长文本定位问题以及获得更精确的文本区域定位。\n",
     "<center><img src=\"https://ai-studio-static-online.cdn.bcebos.com/e90adf3ca25a45a0af0b84a181fbe2c4954be1fcca8f4049957128548b7131ef\"\n",
     "width=\"1000\" ></center>\n",
     "<br><center>图11 LOMO框架图</center>\n",
@@ -228,7 +228,7 @@
     "collapsed": false
    },
    "source": [
-    "## 3. 总结\n",
+    "## 3 总结\n",
     "\n",
     "本节介绍了近几年来文本检测领域的发展，包括基于回归、分割的文本检测方法，并分别列举并介绍了一些经典论文的方法思路。下一节以PaddleOCR开源库为例，详细介绍DBNet的算法原理以及核心代码实现。"
    ]

notebook/notebook_ch/如何使用本书.ipynb

@@ -42,7 +42,7 @@
     "\n",
     "然后安装第三方库：\n",
     "\n",
-    "```\n",
+    "```bash\n",
     "cd PaddleOCR\n",
     "pip3 install -r requirements.txt\n",
     "```\n",

notebook/notebook_en/2.text_detection/text_detection_FAQ.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Text detection FAQ\n",
+    "\n",
+    "This section lists some of the problems that developers often encounter when using PaddleOCR's text detection model, and gives corresponding solutions or suggestions.\n",
+    "\n",
+    "The FAQ is introduced in two parts, namely:\n",
+    "  -Text detection training related\n",
+    "  -Text detection and prediction related"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. FAQ about Text Detection Training\n",
+    "\n",
+    "**1.1 What are the text detection algorithms provided by PaddleOCR?**\n",
+    "\n",
+    "**A**: PaddleOCR contains a variety of text detection models, including regression-based text detection methods EAST and SAST, and segmentation-based text detection methods DB, PSENet.\n",
+    "\n",
+    "\n",
+    "**1.2: What data sets are used in the Chinese ultra-lightweight and general models in the PaddleOCR project? How many samples were trained, what configuration of GPUs, how many epochs were run, and how long did they run?**\n",
+    "\n",
+    "**A**: For the ultra-lightweight DB detection model, the training data includes open source data sets lsvt, rctw, CASIA, CCPD, MSRA, MLT, BornDigit, iflytek, SROIE and synthetic data sets, etc. The total data volume is 10W, The data set is divided into 5 parts. A random sampling strategy is used during training. The training takes about 500 epochs on a 4-card V100GPU, which takes 3 days.\n",
+    "\n",
+    "\n",
+    "**1.3 Does the text detection training label require specific text labeling? What does the \"###\" in the label mean?**\n",
+    "\n",
+    "**A**: Text detection training only needs the coordinates of the text area. The label can be four or fourteen points, arranged in the order of upper left, upper right, lower right, and lower left. The label file provided by PaddleOCR contains text fields. For unclear text in the text area, ### will be used instead. When training the detection model, the text field in the label will not be used.\n",
+    " \n",
+    "**1.4 Is the effect of the text detection model trained when the text lines are tight?**\n",
+    "\n",
+    "**A**: When using segmentation-based methods, such as DB, to detect dense text lines, it is best to collect a batch of data for training, and during training, a binary image will be generated [shrink_ratio](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/ppocr/data/imaug/make_shrink_map.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L37)Turn down the parameter. In addition, when forecasting, you can appropriately reduce [unclip_ratio](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L59) parameter, the larger the unclip_ratio parameter value, the larger the detection frame.\n",
+    "\n",
+    "\n",
+    "**1.5 For some large-sized document images, DB will have more missed inspections during inspection. How to avoid this kind of missed inspections?**\n",
+    "\n",
+    "**A**: First of all, you need to determine whether the model is not well-trained or is the problem handled during prediction. If the model is not well trained, it is recommended to add more data for training, or add more data to enhance it during training.\n",
+    "If the problem is that the predicted image is too large, you can increase the longest side setting parameter [det_limit_side_len] entered during prediction [det_limit_side_len](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L47), which is 960 by default.\n",
+    "Secondly, you can observe whether the missed text has segmentation results by visualizing the post-processed segmentation map. If there is no segmentation result, the model is not well trained. If there is a complete segmentation area, it means that it is a problem of post-prediction processing. In this case, it is recommended to adjust [DB post-processing parameters](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L51-L53)。\n",
+    "\n",
+    "\n",
+    "**1.6 The problem of missed detection of DB model bending text (such as a slightly deformed document image)?**\n",
+    "\n",
+    "**A**: When calculating the average score of the text box in the DB post-processing, it is the average score of the rectangle area, which is easy to cause the missed detection of the curved text. The average score of the polygon area has been added, which will be more accurate, but the speed is somewhat different. Decrease, can be selected as needed, and you can view the [Visual Contrast Effect] (https://github.com/PaddlePaddle/PaddleOCR/pull/2604) in the relevant pr. This function is selected by the parameter [det_db_score_mode](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/tools/infer/utility.py#L51), the parameter value is optional [`fast` (default) , `slow`], `fast` corresponds to the original rectangle mode, and `slow` corresponds to the polygon mode. Thanks to the user [buptlihang](https://github.com/buptlihang) for mentioning [pr](https://github.com/PaddlePaddle/PaddleOCR/pull/2574) to help solve this problem.\n",
+    "\n",
+    "\n",
+    "**1.7 For simple OCR tasks with low accuracy requirements, how many data sets do I need to prepare?**\n",
+    "\n",
+    "**A**: (1) The amount of training data is related to the complexity of the problem to be solved. The greater the difficulty and the higher the accuracy requirements, the greater the data set requirements, and in general, the more training data in practice, the better the effect.\n",
+    "\n",
+    "(2) For scenes with low accuracy requirements, the amount of data required for detection tasks and recognition tasks is different. For inspection tasks, 500 images can guarantee the basic inspection results. For recognition tasks, it is necessary to ensure that the number of line text images in which each character in the recognition dictionary appears in different scenes needs to be greater than 200 (for example, if there are 5 words in the dictionary, each word needs to appear in more than 200 pictures, then The minimum required number of images should be between 200-1000), so that the basic recognition effect can be guaranteed.\n",
+    "\n",
+    "\n",
+    "**1.8 How to get more data when the amount of training data is small?**\n",
+    "\n",
+    "**A**: When the amount of training data is small, you can try the following three ways to get more data: (1) Collect more training data manually, the most direct and effective way. (2) Basic image processing or transformation based on PIL and opencv. For example, the three modules of ImageFont, Image, ImageDraw in PIL write text into the background, opencv's rotating affine transformation, Gaussian filtering and so on. (3) Synthesize data using data generation algorithms, such as algorithms such as pix2pix.\n",
+    "\n",
+    "\n",
+    "**1.9 How to replace the backbone of text detection/recognition?**\n",
+    "\n",
+    "A: Whether it is text detection or text recognition, the choice of backbone network is a trade-off between prediction effect and prediction efficiency. Generally, if you choose a larger-scale backbone network, such as ResNet101_vd, the detection or recognition will be more accurate, but the prediction time will increase accordingly. However, choosing a smaller-scale backbone network, such as MobileNetV3_small_x0_35, will predict faster, but the accuracy of detection or recognition will be greatly reduced. Fortunately, the detection or recognition effects of different backbone networks are positively correlated with the image 1000 classification task in the ImageNet dataset. PaddleClas, a flying paddle image classification suite, summarizes 23 series of classification network structures such as ResNet_vd, Res2Net, HRNet, MobileNetV3, GhostNet, etc. The top1 recognition accuracy rate of the above image classification task, GPU (V100 and T4) and CPU (Snapdragon 855) The prediction time-consuming and the corresponding 117 pre-training model download addresses.\n",
+    "\n",
+    "(1) The replacement of the text detection backbone network is mainly to determine 4 stages similar to ResNet to facilitate the integration of subsequent detection heads similar to FPN. In addition, for the text detection problem, the classification pre-training model trained by ImageNet can accelerate the convergence and improve the effect.\n",
+    "\n",
+    "(2) The replacement of the backbone network for text recognition requires attention to the drop position of the network width and height stride. Since text recognition generally has a large ratio of width to height, the frequency of height reduction is less, and the frequency of width reduction is more. You can refer to [Changes to the MobileNetV3 backbone network in PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.3/ppocr/modeling/backbones/rec_mobilenet_v3.py)。\n",
+    "\n",
+    "\n",
+    "**1.10 How to finetune the detection model, such as freezing the previous layer or learning with a small learning rate for some layers?**\n",
+    "\n",
+    "**A**: If you freeze certain layers, you can set the stop_gradient property of the variable to True, so that all the parameters before calculating this variable will not be updated, refer to: https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/faq/train_cn.html#id4\n",
+    "\n",
+    "If learning with a smaller learning rate for some layers is not very convenient in the static graph, one method is to set a fixed learning rate for the weight attribute when the parameters are initialized, refer to: https://www.paddlepaddle.org.cn/documentation/docs/en/develop/api/paddle/fluid/param_attr/ParamAttr_cn.html#paramattr\n",
+    "\n",
+    "In fact, our experiment found that directly loading the model to fine-tune without setting different learning rates of certain layers, the effect is also good.\n",
+    "\n",
+    "**1.11 In the preprocessing part of DB, why should the length and width of the picture be processed into multiples of 32?**\n",
+    "\n",
+    "**A**: It is related to the stride of the network downsampling. Take the resnet backbone network under inspection as an example. After the image is input to the network, it needs to be downsampled by 2 times for 5 times, a total of 32 times. Therefore, it is recommended that the input image size be a multiple of 32.\n",
+    "\n",
+    "\n",
+    "**1.12 In the PP-OCR series models, why does the backbone network for text detection not use SEBlock?**\n",
+    "\n",
+    "**A**: The SE module is an important module of the MobileNetV3 network. Its purpose is to estimate the importance of each feature channel of the feature map, assign weights to each feature of the feature map, and improve the expressive ability of the network. However, for text detection, the resolution of the input network is relatively large, generally 640\\*640. It is difficult to use the SE module to estimate the importance of each feature channel of the feature map. The network improvement ability is limited, but the module is relatively time-consuming. In the PP-OCR system, the backbone network for text detection does not use the SE module. Experiments also show that when the SE module is removed, the size of the ultra-lightweight model can be reduced by 40%, and the text detection effect is basically not affected. For details, please refer to the PP-OCR technical article, https://arxiv.org/abs/2009.09941.\n",
+    "\n",
+    "\n",
+    "**1.13 The PP-OCR detection effect is not good, how to optimize it?**\n",
+    "\n",
+    "**A**: Specific analysis of specific issues:\n",
+    "- If the detection effect is not available on your scene, the first choice is to do finetune training on your data;\n",
+    "- If the image is too large and the text is too dense, it is recommended not to over-compress the image. You can try to modify the resize logic of the detection preprocessing to prevent the image from being over-compressed;\n",
+    "- The size of the detection frame is too close to the text or the detection frame is too large, you can adjust the db_unclip_ratio parameter, increasing the parameter can enlarge the detection frame, and reducing the parameter can reduce the size of the detection frame;\n",
+    "- There are many missed detection problems in the detection frame, which can reduce the threshold parameter det_db_box_thresh for DB detection to prevent some detection frames from being filtered out. You can also try to set det_db_score_mode to'slow';\n",
+    "- Other methods can choose use_dilation as True to expand the feature map of the detection output. In general, the effect will be improved.\n",
+    "\n",
+    "\n",
+    "## 2. FAQ about Text Detection and Prediction\n",
+    "\n",
+    "**2.1 In DB, some boxes are too pasted with text, but some corners of the text are removed to affect the recognition. Is there any way to alleviate this problem?**\n",
+    "\n",
+    "**A**: The post-processing parameter [unclip_ratio](https://github.com/PaddlePaddle/PaddleOCR/blob/d80afce9b51f09fd3d90e539c40eba8eb5e50dd6/tools/infer/utility.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L52) can be appropriately increased. the larger the parameter, the larger the text box.\n",
+    "\n",
+    "\n",
+    "**2.2 Why does the PaddleOCR detection prediction only support one image test? That is, test_batch_size_per_card=1**\n",
+    "\n",
+    "**A**: When predicting, the image is scaled in equal proportions, the longest side is 960, and the length and width of different images after scaling in equal proportions are inconsistent, and they cannot form a batch, so set test_batch_size to 1.\n",
+    "\n",
+    "\n",
+    "**2.3 Accelerate PaddleOCR's text detection model prediction on the CPU?**\n",
+    "\n",
+    "**A**: x86 CPU can use mkldnn (OneDNN) for acceleration; enable [enable_mkldnn](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py#L105) Parameters. In addition, in conjunction with increasing the number of threads used for prediction on the CPU, [num_threads](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py#L106) can effectively speed up the prediction speed on the CPU.\n",
+    "\n",
+    "**2.4 Accelerate PaddleOCR's text detection model prediction on GPU?**\n",
+    "\n",
+    "**A**: TensorRT is recommended for GPU accelerated prediction.\n",
+    "- 1. Download the Paddle installation package or prediction library with TensorRT from [link](https://paddleinference.paddlepaddle.org.cn/master/user_guides/download_lib.html).\n",
+    "- 2. Download the [TensorRT](https://developer.nvidia.com/tensorrt) from the Nvidia official website. Note that the downloaded TensorRT version is consistent with the TensorRT version compiled in the paddle installation package.\n",
+    "- 3. Set the environment variable `LD_LIBRARY_PATH` to point to the lib folder of TensorRT\n",
+    "```\n",
+    "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<TensorRT-${version}/lib>\n",
+    "```\n",
+    "- 4. Enable [tensorrt option](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py?_pjax=%23js-repo-pjax-container%2%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L38).\n",
+    "\n",
+    "**2.5 How to deploy PaddleOCR model on the mobile terminal?**\n",
+    "\n",
+    "**A**: Flying Oar Paddle has a special tool for mobile deployment [PaddleLite](https://github.com/PaddlePaddle/Paddle-Lite), and PaddleOCR provides DB+CRNN as the demo android arm deployment code , Refer to [link](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.3/deploy/lite/readme.md).\n",
+    "\n",
+    "\n",
+    "**2.6 How to use PaddleOCR multi-process prediction?**\n",
+    "\n",
+    "**A**: PaddleOCR recently added [Multi-Process Predictive Control Parameters](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L111), `use_mp` indicates whether When using multiple processes, `total_process_num` indicates the number of processes when using multiple processes. For specific usage, please refer to [document](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.3/doc/doc_ch/inference.md#1-%E8%B6%85%E8%BD%BB%E9%87%8F%E4%B8%AD%E6%96%87ocr%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86).\n",
+    "\n",
+    "**2.7 Video memory explosion and memory leak during prediction?**\n",
+    "\n",
+    "**A**: If it is the prediction of the training model, the video memory is not enough because the model is too large or the input image is too large, you can refer to the code and add paddle.no_grad() before the main function runs to reduce the video memory usage. If the memory usage of the inference model is too high, you can add [config.enable_memory_optim()](https://github.com/PaddlePaddle/PaddleOCR/blob/8b656a3e13631dfb1ac21d2095d4d4a4993ef710/tools/infer/utility.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L267) to reduce the memory usage when configuring Config.\n",
+    "\n",
+    "In addition, regarding the memory leak when using Paddle to predict, it is recommended to install the latest version of paddle. The memory leak has been fixed."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
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+}

notebook/notebook_en/how_to_use_these_notebooks.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# 1. Course Prerequisites\n",
+    "\n",
+    "The OCR model involved in this course is based on deep learning, so its related basic knowledge, environment configuration, project engineering and other materials will be introduced in this section, especially for readers who are not familiar with deep learning. content.\n",
+    "\n",
+    "### 1.1 Preliminary Knowledge\n",
+    "\n",
+    "The \"learning\" of deep learning has been developed from the content of neurons, perceptrons, and multilayer neural networks in machine learning. Therefore, understanding the basic machine learning algorithms is of great help to the understanding and application of deep learning. The \"deepness\" of deep learning is embodied in a series of vector-based mathematical operations such as convolution and pooling used in the process of processing a large amount of information. If you lack the theoretical foundation of the two, you can learn from teacher Li Hongyi's [Linear Algebra](https://aistudio.baidu.com/aistudio/course/introduce/2063) and [Machine Learning](https://aistudio.baidu.com/aistudio/course/introduce/1978) courses.\n",
+    "\n",
+    "For the understanding of deep learning itself, you can refer to the zero-based course of Bai Ran, an outstanding architect of Baidu: [Baidu architects take you hands-on with zero-based practice deep learning](https://aistudio.baidu.com/aistudio/course/introduce/1297), which covers the development history of deep learning and introduces the complete components of deep learning through a classic case. It is a set of practice-oriented deep learning courses.\n",
+    "\n",
+    "For the practice of theoretical knowledge, [Python basic knowledge](https://aistudio.baidu.com/aistudio/course/introduce/1224) is essential. At the same time, in order to quickly reproduce the deep learning model, the deep learning framework used in this course For: Flying PaddlePaddle. If you have used other frameworks, you can quickly learn how to use flying paddles through [Quick Start Document](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/quick_start/hello_paddle.html).\n",
+    "\n",
+    "### 1.2 Basic Environment Preparation\n",
+    "\n",
+    "If you want to run the code of this course in a local environment and have not built a Python environment before, you can follow the [zero-base operating environment preparation](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/environment.md), install Anaconda or docker environment according to your operating system.\n",
+    "\n",
+    "If you don't have local resources, you can run the code through the AI Studio training platform. Each item in it is presented in a notebook, which is convenient for developers to learn. If you are not familiar with the related operations of Notebook, you can refer to [AI Studio Project Description](https://ai.baidu.com/ai-doc/AISTUDIO/0k3e2tfzm).\n",
+    "\n",
+    "### 1.3 Get and Run the Code\n",
+    "\n",
+    "This course relies on the formation of PaddleOCR's code repository. First, clone the complete project of PaddleOCR:\n",
+    "\n",
+    "```bash\n",
+    "# [recommend]\n",
+    "git clone https://github.com/PaddlePaddle/PaddleOCR\n",
+    "\n",
+    "# If you cannot pull successfully due to network problems, you can also choose to use the hosting on Code Cloud:\n",
+    "git clone https://gitee.com/paddlepaddle/PaddleOCR\n",
+    "```\n",
+    "\n",
+    "> Note: The code cloud hosted code may not be able to synchronize the update of this github project in real time, there is a delay of 3~5 days, please use the recommended method first.\n",
+    ">\n",
+    "> If you are not familiar with git operations, you can download the compressed package directly from the `Code` on the homepage of PaddleOCR\n",
+    "\n",
+    "Then install third-party libraries:\n",
+    "\n",
+    "```bash\n",
+    "cd PaddleOCR\n",
+    "pip3 install -r requirements.txt\n",
+    "```\n",
+    "\n",
+    "\n",
+    "\n",
+    "### 1.4 Access to Information\n",
+    "\n",
+    "[PaddleOCR Usage Document](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/README.md) describes in detail how to use PaddleOCR to complete model application, training and deployment. The document is rich in content, most of the user’s questions are described in the document or FAQ, especially in [FAQ](https://github.com/PaddlePaddle/PaddleOCR/blob/release%2F2.3/doc/doc_en/FAQ_en.md), in accordance with the application process of deep learning, has precipitated the user's common questions, it is recommended that you read it carefully.\n",
+    "\n",
+    "### 1.5 Ask for Help\n",
+    "\n",
+    "If you encounter BUG, ease of use or documentation related issues while using PaddleOCR, you can contact the official via [Github issue](https://github.com/PaddlePaddle/PaddleOCR/issues), please follow the issue template Provide as much information as possible so that official personnel can quickly locate the problem. At the same time, the WeChat group is the daily communication position for the majority of PaddleOCR users, and it is more suitable for asking some consulting questions. In addition to the PaddleOCR team members, there will also be enthusiastic developers answering your questions."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "py35-paddle1.2.0"
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+    "name": "ipython",
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+   },
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
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+ "nbformat_minor": 4
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